Electromechanical Impedance-Based Damage Detection Using Machine Learning Approaches

Electromechanical impedance-based structural health monitoring has been the subject of extensive research in recent decades. The method’s low cost and ability to detect minor structural damages make it an appealing alternative to other non-destructive techniques. Ongoing research on damage detection approaches continues to be a topic of interest in relation to the electromechanical impedance method. This work proposes the use of the K-Means, Decision Tree, and Random Forest algorithms to distinguish between four structural conditions in an aluminum beam. These techniques were applied to raw impedance data and a dataset reduced via principal components analysis. The findings revealed that the compressed dataset improved the accuracy of all models, except for the Random Forest approach, whose accuracy decreased by 2.9%. The K-Means algorithm was most affected by the reduction in dimensionality, with a 105.9% increase in accuracy. The Decision Tree and Random Forest methods yielded outstanding outcomes, comparable or superior to other state-of-the-art approaches. This makes them a compelling choice for detecting damage using electromechanical impedance data, even when using raw data as the input information.